Ian Quigley

Cofounder, @LeashBio.

[Michaela Hinks @MichaelaThinks]

Unironically, it is so good that biology is cool now. Everyone should be rooting for AI to cure all disease. Stop speculating about whether it will or will not happen and ask yourself what you personally can do to make it happen. Then do it

[Xenbase @Xenbase]

Nikon Small World 2026 Photomicrography Competition: 16th Place, Embryonic Xenopus skin cells - cilia (green), centrioles (red), cell membranes (white) Nouaux et al. IBDM Marseille, France https://t.co/i1mYMxAw4L #science #devbio #cellbio #xenopus #frogs

[Nature Reviews Drug Discovery @NatRevDrugDisc]

The expansion of kinase drug discovery: new targets, platforms and indications
https://t.co/Zve3ii4XX2
https://t.co/dTcsMAKBsj

This new Review analyses the kinase therapeutic landscape, covering more than 2,100 kinase-targeting agents that have entered clinical development

[Ian Quigley @allmeasures]

@Ronalfa Everything goes so much faster now

[Ryan York @ryanayork]

The part that matters if you’re betting on one layer: Growth is the MOST heritable, yet RNA and protein together recover only ~25% of its recoverable variation. Most of what determines organismal performance isn’t visible in the molecular layers we measure most. [9/11]

[J.B. Sauceda @jaybsauceda]

Tonight I leave for Terlingua, home of the granddaddy of all chili cook-offs which is a great opportunity to remind you that beans do not belong in chili you heathens

[Ian Quigley @allmeasures]

Yep, we think this too. If we want superhuman performance, agents might need to learn from measurements of nature and not human graders.

[Nima Alidoust @nalidoust]

What makes a biological reasoning task verifiable? Our team @tahoe_ai has spent the last few months on that question and landed on one conviction: for human-difficult tasks with verifiable rewards, the answer key has to come from ground-truth experimental data.

Along the way we built thousands of such environments, which we call TahoeMysts, and we use them to evaluate frontier models (among other things). Every so often a new model produces a big jump, and it is always exciting. Today is one of those days: @claudeai Mythos 5.2!

We will say more about this work in the future. In the meantime, take a look at our results today.

[Ron Alfa @Ronalfa]

"Drug development isn’t rocket science, its actually much harder." 🔥

[Ian Quigley @allmeasures]

When next-generation sequencing came out, the thing to do was to read all those Nucleic Acid Research papers from the 1990s looking at a single gene and extending the study to *all* genes. This sorta rhymes.

[Nathan C. Frey @nc_frey]

With this model release, Claude is beginning to feel like a very capable assistant for research. One exercise I can recommend is sharing an older paper or draft, and asking the model to reimplement or extend it in some way. Claude Mythos 5.2 excels on long-horizon research tasks.

I ask all our models to hypothesize about the function of vault particles now. Here’s what Mythos 5.2 thinks:

[Ian Quigley @allmeasures]

@jungofthewon @awscloud @Ginkgo @dnanexus @leashbio for research!

[Ian Quigley @allmeasures]

I used to think one day a camera in your bathroom mirror might give a daily illness eval but one facing your front door or kitchen might be better

[Jorge Bravo Abad @bravo_abad]

AI finds health signals in movement beyond age and body size

A model that predicts health from how someone moves might mostly be recognizing their age and body size. Those factors already explain many differences in health.

Gabet and colleagues put that possibility to the test. They recorded five movement tasks in 3,328 adults using a depth camera, then trained a masked autoencoder to reconstruct missing parts of the 3D skeletal motion. The model learned patterns of movement without needing health labels during pretraining.

The crucial comparison came afterward. Could those learned patterns improve health predictions when the baseline already knew each person’s age, BMI, height and abdominal fat? They evaluated males and females separately.

In male participants, the learned movement features significantly improved prediction for 823 of 3,210 measured traits, compared with 188 for conventional movement measurements such as cadence and stride length. The model was recovering information that those familiar summaries missed.

These were mainly associations with health measured at the same time, within one cohort. They do not yet establish that the model can predict future disease or work reliably in other populations.

For scientists developing new biomarkers, this is a useful test: give the baseline the obvious explanations first, then measure what your learned representation adds.

Gabet et al., Nature Medicine (2026). Journal license. https://t.co/z17XEhwVUI

[Adu @plainyogurt21]

>be postdoc
>2012 in Cambridge
>join Warp Drive Bio
>plan to mine bacterial genomes
>find macrocycles
>what is a macrocycle
>RAS is undruggable
>everyone says so
>surface is a Death Star
>smooth no pockets
>picomolar grip on GTP
>whole field races the OFF state
>bossman crazy
>says hit the ON state
>the active one
>soil bug makes sanglifehrin
>CypA just sitting in the cell
>free chaperone?
>what if we glue it?
>macrocycle binds CypA
>new surface grabs RAS
>only the GTP bound state
>it clamps
>signal dies
>pure three way complex
>years of wet lab work
>first compound is garbage
>micromolar
>aim only at conserved residues
>swap phenyl for thiazole
>better CypA contact
>potency jumps
>no bioavailability
>unfixable half life
>fix it anyway
>chop the right side to a cyclopropyl
>stick a methylpiperazine on the left
>affinity explodes
>811 daltons
>big chungus
>breaks all rules
>should not be oral
>it is oral
>2018 buyout
>molecule becomes RMC-6236
>early data looks promising
>vp writes the blog post
>doesn't get it
>2023 need cash for phase 3
>EQRx dying with 1.1B in the bank
>buy the company for the cash
>sent CRO our structures to make
>they like
>they steal
>joyo to erasca
>where compound 4a
>comes back as ERAS-0015
>RASolute 302 reads out
>13.2 months
>double chemo SoC
>ASCO plenary
>1 min standing ovation
>presenter angry face
>senator on expanded access
>calls it a miracle drug
>press goes wild
>rash nbd
>dying is worse
>approval August 2026 easy
>198 million in five weeks
>mfw a macrocycle, from a dead startup, funded by a dead company, generational drug

[gokul gowri @gokl_g]

the transcriptional state of a cell is not generally sufficient to predict its past or future. in our new preprint we show that conditioning on lineage context (sisters, cousins, etc) can help a lot.

this one was v fun :)

https://t.co/nmlsV3o1fP

[Aesther @AestherML]

This is a Zebrafish

It has the same amount of Synapses as Qwen 3.8 27B but lacks the ability to one-shot Minecraft in Three.js

Disappointing

[Kate Stafford @kastacholamine]

Interesting - where are LLMs close to SOTA for small molecules in your experience?

There's this which I was very impressed by - https://t.co/Qp19wxgDqT. Inspired by that post, I did a similar but simpler experiment using docking scores with similar results. And the Leash Bio example of using DEL screens for search was great. https://t.co/TDNAa5jIPx

But there's also a lot of closely adjacent tasks where LLMs are still pretty bad. And even on these successful tasks, they're pretty slow.

[Jacob Lauritzen @jacsebl]

Got fable to hand-write weights of a 100k param model to recognize real versus fake legal citations.

Basically one-shotted a full NN that’s better than training it

[Ron Alfa @Ronalfa]

We ran a pipeline in a person team at Recursion in 2021 to start the oncology work. Pre-LLM we could execute quickly outsourcing at scale - the models will make this much more efficient. But the game changer was scaled biological insights from the platform, this is still the gap.

[Alex Krentsel @AlexKrentsel]

i've reproduced the results from the MIT blog post today on weight-space programming. but one additional result i have to add: this is a new, step-change emerging capability that's appeared in just the latest set of models.

[Ellen Zhong @ZhongingAlong]

Can we help discover new, unknown small molecules using modern generative AI and scientific agents?

Excited to share our work, led by superstar @zyxiong123, on ChefNMR2🧑‍🍳⚛️, a new approach for structure elucidation from NMR spectra!

Paper: https://t.co/OepVzmW2g5

🧵 1/

[Science Magazine @ScienceMagazine]

Carrion crows make decisions faster than monkeys and humans.

Learn more in a new #SciencePerspective: https://t.co/9tRcRvu3fb